Event entity monitoring network and method

ABSTRACT

A method of monitoring for the presence of an event entity in a monitored region comprising receiving, at a first level of detail, first event data from at least one data processing device configured to monitor at least a portion of a monitored region, the first event data indicative of an event entity occurring in the monitored region and being an anonymized compression of raw information data collected by said at least one data processing device; processing the first event data to determine the presence of an event entity indicated by the event data; requesting, at a second level of detail, second event data, wherein the second level of detail is more detailed than the first level of detail; and receiving a notification comprising the second event data having the second level of detail. The second level of detail is requested in response to the event entity matching a notification event, and a recognition server has authorization to access the second event data having the second level of detail. Alternatively, the second level of detail is requested in response to the event entity matching a notification event, and a recognition server has a policy to access the second event data having the second level of detail.

This application is a continuation of U.S. application Ser. No.16/502,465 filed Jul. 3, 2019, which claims priority to GB PatentApplication No. 1810971.0 filed Jul. 4, 2018, the entire contents ofeach of which are incorporated herein by reference in this application.

The present technology relates to a network and a method of monitoringfor the presence of an event entity. Such monitoring enables scenariorecognition and can result in a wide set of actions being implemented bya system in response to the monitoring. Such actions may be an alert,alarm or a security response, a change of infrastructure, coursecorrection, behaviour modification of machines or logging events,recording evidence.

The present technology also provides for analysis of event entities atvarious levels of abstraction. Such abstraction can speed up recognitionalgorithms used in scenario recognition and provide improved securitythrough anonymity of data.

The present technology also provides an option for varying the level ofabstraction of data provided to a remote user dependent uponpredetermined criteria or automatic, on-the-fly based variation. Presenttechniques have particular application in physical and electronicsecurity networks of electronic devices. In such a case the system mayimplement an action of encryption of data or adaptive resolutionsampling of the event entity depending upon user protocols.

Scenario recognition is a process of identifying event entities incontext. Event entities may be determined from gathering event data froma wide range of monitoring sources. For example, identification of afootball with people (event data) in an environment such as a stadium(further event data) leads to a high probability that a football matchis occurring and that the scenario is one of a group of people playingfootball. Within the football match event entity there are many evententities that can be monitored and/or inferred from event data. Theseother event entities may be a dropped ball, dangerous play, a tackle, afire or a crowd disturbance.

Present techniques provide a scenario recognition service forintegration into an Internet of Things (IoT) networked environment. Moreand more data processing devices are being connected together, often viathe cloud, as part of the Internet of Things (IoT). Such devices may beembedded IoT devices, including image sensors, sound sensors, brightnesssensors, odour sensors, temperature sensors, humidity sensors andproximity sensors, fitness trackers, PIR motion detectors and mobile(cell) phones. Applied to the above example of the football match event,an IoT temperature sensor may indicate a high ambient temperature and sothe scenario may be more accurately described as one of people playingfootball during a hot day.

According to a first technique, there is provided a method of monitoringfor the presence of an event entity in a monitored region comprisingreceiving, at a first level of detail, first event data from at leastone data processing device of a plurality of data processing deviceseach configured to monitor at least a portion of a monitored region, thefirst event data indicative of an event entity occurring in themonitored region; processing the first event data to determine thepresence of an event entity indicated by the event data; comparing theidentified event entity with a data store defining notification events;and responsive to the identified event entity matching a notificationevent, outputting a notification relating to the identified evententity.

Present techniques provide a method that delivers both security,scalability and efficiencies needed for mass adoption, especially withconstrained communication and IoT communication protocols.

Embodiments will now be described, with reference to the accompanyingdrawings of which:

FIG. 1 is a schematic diagram of a scenario recognition networkaccording to presently described technology;

FIG. 2 is a schematic diagram of a recognition server forming part of ascenario recognition network according to presently describedtechnology; and

FIG. 3 is a flow diagram of a communications method according topresently described technology.

Scenario recognition systems process images in order to gain informationabout the real world. Such a recognition system is required to acquire,process, analyse and understand digital images in order to extract datafrom the digital images. The recognition system normally comprises oneor more image acquisition devices, such as cameras, for obtaining thedigital images.

In order to “understand” a digital image, such a recognition system isrequired to detect features and objects within the image, for example bydetecting lines, edges, ridges, corners, blobs, textures, shapes,gradients, regions, boundaries, surfaces, volumes, colours and shadings.The detected features are used to understand the image the recognitionsystem is looking at, such as the size and shape of the scenario, aswell as identify different objects within the scenario and the geometryand orientation of those objects.

Recognition systems are designed to extract high-level symbolicinformation from raw image or video data, that can be used by a softwareprogram to accomplish some task. Examples of high-level informationinclude spatial models of the scenario, lists of objects in thescenario, identifications of unique objects in a scenario, tracking ofobjects though a space, estimation of the motion of objects in ascenario, detection of events in a scenario, recognition of gestures.Common applications of this information include indexing, userinterfaces, surveillance, augmented reality, text recognition, processcontrol, inspection / diagnosis, navigation.

Recognition can be accomplished using a variety of methods. Tasksrequiring object recognition are usually solved by some variation on aprocess of comparing stored representations of objects of interest toelements in the scenario then applying some rule for determining amatch. Many recognition systems therefore rely on a proprietary datastore of pre-specified objects when trying to identify a detectedobject. For example, a recognition system may group a set of features asa candidate object in the scenario and then refers to the data store ofpre-specified objects in order to identify the detected object. Theproprietary data store, sometimes called a modelbase, of pre-specifiedobjects, sometimes called templates, is associated with the recognitionsystem and comprises a plurality of images of different objects.

In a first technique, a method of monitoring an event entity comprisesreceiving, at a first level of detail, first event data from a pluralityof data processing devices in a monitored region; processing the firstevent data to identify the event entity inferred by the event data;comparing the identified event entity with a data store definingnotification events; and responsive to the identified event entitymatching a notification event, outputting a notification.

In embodiments, the sensing system comprises sensors such as a camera,drone, autonomous vehicle or many forms of IoT sensor (light, smell,temperature, sound etc) which can extract data of image size,orientation, type, ID and tracking in real time, and process that datausing AI/ML at the edge, then send a compressed output to the cloud. Theevent entity inferred by the event data may be considered as a localinference or determination that is in fact smaller in data size than theoriginal raw data. So, the data output by a sensor may be anonymiseddata or a notification, but also in embodiments may be a coarseinference (i.e. by some Machine Learning classification process)relating to an event that has occurred.

Therefore, present techniques may go further than compressing rawinformation and may empower devices to make some local inference aboutthe sensed environment, for example where there is a desire to reach aquick, coarse first determination. Notification may be an output from aclassifier that is attempting to determine whether an event occurs andtherefore using machine learning of data sets not just for compression,but also to allow determination and interrogation of an event entity todetermine whether the event occurs or not.

Accordingly, in techniques a compressed data output may be anonymiseddata, a notification, metadata at varying levels of abstraction. Inexamples, only the notification leaves the edge, the data can stayencrypted locally. The event entity therefore may be represented inembodiments as a compression of the raw information data which is sensedby the sensing system. The data transmitted from the local sensor is ata level of detail which is lower than the raw level of detail beingactively sensed.

An edge sensor may be configured to implement a Machine LearningAlgorithm (MLA) and may also carry out various methods in order to trainthe MLA. In some embodiments, the MLA may be one of: an artificialneural network, a Bayesian network or a support vector machine. Inanother embodiment, the MLA may be a prediction model that consists ofan ensemble of decision trees for solving inter alia regression andclassification problems. Different algorithms may be used for generatingnotifications for varying policies at different levels of abstraction.Natural language processing may be used in combination with imageanalysis for example, also at different levels of abstraction.

In embodiments, the first level of detail of first event data comprisesmetadata describing properties of the event entity. The metadata may bea message to state that an event has occurred, for example, according toa predefined list of possible events and associated messages. Themessage may be time and date stamped, for example, and include ageolocation tag. In such a case, the event entity is identified basedupon the metadata. In cases, a predetermined course of action is madebased upon the metadata.

In embodiments, in response to the event entity matching a notificationevent, requesting at a second level of detail second event data, whereinthe second level of detail is more detailed than the first level ofdetail, and outputting a notification comprising the event data havingthe second level of detail. In such a case, outputting a notificationcomprises the event data having the second level of detail comprises anotification to a data processing device having authorisation to accessthe event data having the second level of detail.

The event data may take many forms. For example, the event data mayidentify a movement of the event entity, an image of the event entity, asound of the event entity, a common grouping or class of related evententities or a proximity value between event entities, the proximityvalue being a pre-defined distance between monitored event entities.

Embodiments include communicating the event data to a recognitionserver. The recognition server may perform vector form extraction on theevent data to provide the first level of detail. In such a case, thefirst level of detail is a vector image representative of the eventdata.

The recognition server may comprise a scenario identifier incommunication with a decision module for determining a course of actionassociated with the notification.

The recognition server may also include a scenario identifier incommunication with a vector event data store, a vector scenario datastore and a vector environment data store for comparing the vector imagewith one or more of the data stores.

Embodiments may include a predictive event generator module foranalysing the scenario identifier and making a prediction on a likelyoutcome of a future event. Such a generator may use machine learning andartificial intelligence computing algorithms.

Event data is typically obtained from a plurality of sensors and may inembodiments be fed to the data processing devices from a database ofexisting or historical data. Sensors include at least one of embeddedIoT devices, image sensors, sound sensors, brightness sensors, odoursensors, temperature sensors, humidity sensors and proximity sensors,fitness trackers, PIR motion detectors and mobile (cell) phones.

In embodiments, outputting a notification includes implementing apredetermined course of action. In such a case, the predetermined courseof action is an alert, alarm, security response or tracking the evententity.

Present techniques also provide a monitoring data processing devicecomprising a sensor for monitoring an event entity in a monitored regionand a data processor for generating first event data at a first level ofdetail used to identify the event entity; and an output module forcommunicating the first event data to a recognition server. In such acase, the first level of detail is metadata describing properties of theevent entity, and in embodiments the event entity is identified basedupon the metadata.

The data processing device may also comprise a comparator and a datastore defining notification events; and responsive to the identifiedevent entity matching a notification event, outputting a notification.In such a case, the device may receive a request from a remote server tomonitor an event entity at a second level of detail based on secondevent data, wherein the second level of detail is more detailed than thefirst level of detail, and outputting a notification comprising theevent data having the second level of detail.

Present techniques also provide a recognition server comprising inputcircuitry for receiving, at a first level of detail, first event datafrom a plurality of data processing devices in a monitored region; aprocessor for processing the first event data to identify the evententity inferred by the event data; a comparator for comparing theidentified event entity with a data store defining notification events;and responsive to the identified event entity matching a notificationevent, an output for outputting a notification. In embodiments, thefirst level of detail of first event data is metadata describingproperties of the event entity and the event entity may be identifiedbased upon the metadata.

Present techniques also provide a network for monitoring an event entitycomprising a plurality of data processing devices comprising sensors formonitoring event entities in a monitored region and a data processor forgenerating first event data at a first level of detail used to identifythe event entity; and an output module for communicating the first eventdata to a recognition server; the recognition server comprising inputcircuitry for receiving, at a first level of detail, first event datafrom the plurality of data processing devices; a processor forprocessing the first event data; a comparator for comparing theidentified event entity with a data store defining notification events;and responsive to the identified event entity matching a notificationevent, an output for outputting a notification.

FIG. 1 is a schematic diagram of a scenario recognition network 100according to presently described technology. Referring to FIG. 1, thenetwork 100 comprises many hundreds or thousands of sensors 102, 104,106, 108, 110, 112—sensor n, that identify and capture objects. Objectsare as examples a person, car, fire, alarm, dog, cat and ship from anenvironment 114, 116. A recognition server 118 in communication with thesensors 102—sensor n identifies scenarios or situations occurring in theenvironment 114, 116 based upon data from the one or more sensors102—sensor n capturing data from the environment 114, 116. Within therecognition server 118, the data is processed and raw data may beabstracted to objects locally and objects to a scenario within thenetwork 100, for example to generate from observed objects and via ascenario processing engine (shown in FIG. 2) an abstracted summary ofthe environment. As an output of the recognition server 118, theabstracted scenario may be made available to a variety of users 120, 122to user n.

FIG. 2 is a schematic diagram of the recognition server 118 forming partof the scenario recognition network 100 as shown in FIG. 1 according topresently described technology. Referring to FIG. 2, sensor data 200,which may be at the object level or raw data level depending upon theconfiguration and nature of the network 100 and sensors 100—n iscommunicated to a scenario processing engine 202. The scenarioprocessing engine 202 is connected to an event data store 204 and ascenario data store 206, which are both connected to a predictive eventgenerator module 208. The predictive event generator module is connectedto a user interface 210. The scenario processing engine is furtherconnected to an environment builder module 212, which is connected to anenvironment or node store 214.

The scenario processing engine 202 has a notify 216 and request 218 datacommunication streams connected to the user interface 210. The userinterface 210 is connected to a subscribed scenarios environment module220 which is connected both directly to the scenario processing engine202 and connected to the scenario processing engine 202 by way of analert store 222. In, embodiments, the alert store 222 may be in directcommunication with the notify communication data stream 216. Inembodiments, the scenario processing engine 202 may request sensor data200 from a request communication data channel 224.

In operation, the sensor data 200 is received into the scenarioprocessing engine 202 which processes the data and determines objectsand scenarios based on a data set which may be learnt or provided. Theenvironment builder module 212 allows sensors 102, 104, 106 to be ageographically bound space in an environment 114 such that associationscan be made between sensors 102, 104, 106 to allow scenario data to begenerated. The environment 114 may not be geographically bound, inembodiments the environment 114 may be “my family” or “my fleet ofvehicles”. The subscribed scenarios environment module 220 allows a uservia the user interface 210 to define or choose various environments tosubscribe and/or select triggers of scenarios detection within thoseenvironments. Such environments may be a car accident, assault or fire,for example.

The predictive event generator module 208 is connected to the event datastore 204 and the scenario data store 206. In the scenario where aparticular sequence of events or a particular scenario is identified,the predictive event generator module can make a determination that ascenario outcome has a high probability of occurring. Accordingly, thepredictive event generator module 208 can communication to a userthrough the user interface 210 that a particular event is about tooccur.

In, for example, a specific, but non-limiting, case of monitoring apublic space, by default and to retain privacy, cameras with objectrecognition technology can detect the behaviour of individuals. Such anobject recognition technology is provided by Arm's (RTM) ObjectDetection Processor, which provides images of people, including thedirection they are facing, their movement, pose and even identity. Thisis in real time on scenarios of up to 4K resolution. The digital datarepresenting the people can then be sent for further analysis up stream,either for local processing or to cloud-based services. The coreincludes advanced technology for reducing false positive detection.

In embodiments, a level of abstraction to the data can be provided tothe data obtained by technology such as Arm's (RTM) Object DetectorProcessor. Abstraction can be provided by vector image analysis and canbe used to identify and anonymise people without ever communicatinganything identifying an individual. Scenarios may be communicated as“outside space, people walking”; a security service may be alerted inthe event a scenario is triggered of a person attacking another; or analarm going off in a building.

Applying a level of abstraction to the data such as can be provided byvector image analysis results in smaller data sets enabling fasterprocessing and enabling communication between bandwidth constrained,power constrained and processor constrained embedded IoT devices.

For systems processing and monitoring public areas in which security isless of an issue and IoT devices may have access to more processingpower, the present techniques provide a model in which limited data isavailable for general scenario monitoring, whereas more detailed dataaggregation and visibility can be provided upon request from a user. Inembodiments, the system may automatically determine the environment andcontext from the data obtained at a first level of abstraction anddetail and automatically and dynamically modify the level of abstractionand detail for subsequent data.

A recognition network 100 according to present techniques allows forvarying degrees of abstraction for different environment and users onthe same data simultaneously. For example, for a home alarm monitoringsystem, residents of a neighbourhood can sign up to alert events inneighbouring properties and access full details on their own property.

EXAMPLE 1

Environment=home

Sensor 102, 104, 106=PIR sensors

Sensor 108, 110, 112=cameras

Sensors n=smoke alarms

Sensors n+1=temperature sensor

Sensors n+2=door locks

Identifiable objects=people (PIR/camera), animals, fire (via smokealarm, camera, temperature sensor)

Communicated Scenarios=no fire; fire, no-one home, fire—residents inproperty; fire—persons located in room; unknown person in property

Remote service—remote fire monitoring service that automaticallytriggers an emergency response in the event of a fire, prioritisingbased on risk to life at the property.

FIG. 3 is a flow diagram of a communications method 300 according topresently described technology. For brevity, authentication steps arenot shown. Referring to FIG. 3, user 210 or application subscriber makesa subscription request 302 to the recognition server 116 whilst alsodefining alerts 304. In time, event data is provided 306 from sensor nto the recognition server 118 which communicates scenario data 308 tothe user 210. An alert event or de-abstraction request 310 may be calledto the sensor n and more data at a different level of abstraction iscommunicated 310 to the recognition server 118 for passing through 314to the user 210.

In an alternative embodiment, a user 210 can define alerts locally 316.A user 210 makes a subscription request 318 and the recognition server118 defines the alerts 320. The sensor n provides 322 event data to therecognition server 118 and the user 210 communicates an alert scenario324 to the recognition server 118, which communicates the alert scenario324 as a request 326 to the sensor n, which upon the alert scenario 324being matched returns data 328 to the recognition server 118, which inturn communications the data in the form requested 330 to the user 210.

Various use cases are within the scope of present techniques,particularly in consideration of using a recognition system as a cloudservice. For example, the system may implement a particular policy inresponse to identifying an event entity in an image or video stream.

One example of such a system is where a camera(s) is provided in anoperating theatre to track surgical instruments, whereby when the systemrecognises that an instrument is knocked off a trolley (i.e. the evententity), the policy may be to start recording images, so that ifsomething adverse happens to the patient then the recording can provideevidence of the event; or the policy might be to notify a party that areplacement instrument is required; or to warn the surgical team not touse the instrument.

A policy can be automatic in the sense that it is based uponpredetermined criteria and dynamic in the sense that if the system, forexample, recognises a child in a changing room, it automatically anddynamically sets its policy to the highest level of abstraction andprivacy. If the system recognises an adult falling over in a publicplace, the system may set the policy to the lowest level of abstractionand lowest level of privacy.

Other examples include checking connections to instruments such as fuelpipe delivery systems and if the fuel pipe becomes disconnected duringfuelling then sounding an alert. Monitoring may include proximitydetection where the distance between a baby and a guardian is monitoredand tracked with the notification being an alarm if the distance exceedsa predetermined amount.

A further example is where the camera might track the number of swabsused on a patient and recognise that a swab is missing at the end of anoperation (i.e. the event), and the policy might be to notify thesurgical team that the swab is missing. A further example, was thesystem might detect when a child is being abused and the policy is tonotify police.

Present techniques may be described in the following clauses:

-   Clause 1: A method of monitoring for the presence of an event entity    in a monitored region comprising receiving, at a first level of    detail, first event data from at least one data processing device of    a plurality of data processing devices each configured to monitor at    least a portion of a monitored region, the first event data    indicative of an event entity occurring in the monitored region;    processing the first event data to determine the presence of an    event entity indicated by the event data; comparing the identified    event entity with a data store defining notification events; and    responsive to the identified event entity matching a notification    event, outputting a notification relating to the identified event    entity.-   Clause 2: A method as claimed in clause 1, wherein the first level    of detail of first event data comprises metadata describing    properties of the event entity.-   Clause 3: A method as claimed in clause 2, wherein the event entity    is identified based upon the metadata.-   Clause 4: A method as claimed in clause 1, including requesting at a    second level of detail second event data, wherein the second level    of detail is more detailed than the first level of detail, and    outputting a notification comprising the event data having the    second level of detail; optionally wherein the second level of    detail is requested in response to the event entity matching a    notification event.-   Clause 5: A method as claimed in clause 4, wherein outputting a    notification comprising the event data having the second level of    detail comprises a notification to a data processing device having    authorisation to access the event data having the second level of    detail.-   Clause 6: A method as claimed in clause 1, wherein the event data    identifies a movement of the event entity.-   Clause 7: A method as claimed in clause 1, wherein the event data    identifies an image of the event entity.-   Clause 8: A method as claimed clause 1, wherein the event data    identifies a sound of the event entity.-   Clause 09: A method as claimed in clause 1, wherein the event data    identifies a common grouping or class of related event entities.-   Clause 10: A method as claimed in clause 1, wherein the event data    identifies a proximity value between event entities, the proximity    value being a pre-defined distance between monitored event entities.-   Clause 11: A method as claimed in clause 1, including communicating    the event data to a recognition server.-   Clause 12: A method as claimed in clause 11, including performing    vector form extraction on the event data to provide the first level    of detail.-   Clause 13: A method as claimed in clause 12, wherein the first level    of detail is a vector image representative of the event data.-   Clause 14: A method as claimed in clause 11, wherein the recognition    server comprises a scenario identifier in communication with a    decision module for determining a course of action associated with    the notification.-   Clause 15: A method as claimed in clause 11, wherein the recognition    server comprises a scenario identifier in communication with a    vector event data store, a vector scenario data store and a vector    environment data store for comparing the vector image with one or    more of the data stores.-   Clause 16: A method as claimed in clause 14 or 15, including a    predictive event generator module for analysing the scenario    identifier and making a prediction on a likely outcome of a future    event.-   Clause 17: A method as claimed in clause 11 to 16, including    assessing event data with location data, historical data and/or    environmental conditions data.-   Clause 18: A method as claimed in clause 1, wherein event data is    obtained from a plurality of sensors.-   Clause 19: A method as claimed in clause 18, including combining the    event data from different devices within the plurality of sensors.-   Clause 20: A method as claimed in clause 19, including providing    event data at different levels of detail and processing the event    data at different levels of detail together.-   Clause 21: A method as claimed in clause 18, wherein the sensors    include at least one of embedded IoT devices, image sensors, sound    sensors, brightness sensors, odour sensors, temperature sensors,    humidity sensors and proximity sensors, fitness trackers, PIR motion    detectors and mobile (cell) phones.-   Clause 22: A method as claimed in clause 1, wherein outputting a    notification includes implementing a predetermined course of action.-   Clause 23: A method as claimed in clause 22, wherein the    predetermined course of action is an alert, alarm, security response    or tracking the event entity.-   Clause 24: A method as claimed in any preceding clause, including    implementing a policy in response to identifying an event entity.-   Clause 25: A method as claimed in clause 24, the policy including    one or more of: recording images and/or sound, recording at    different levels of detail or abstraction than the first level of    detail, outputting a notification.-   Clause 26: A method as claimed in clause 24 or 25, including    automatically implementing the policy based upon predetermined    criteria and, optionally, dynamically setting the policy to increase    or decrease a level of abstraction.-   Clause 27: A monitoring data processing device comprising a sensor    for monitoring the presence of an event entity in a monitored region    and a data processor for generating first event data at a first    level of detail used to identify the event entity; and an output    module for communicating the first event data to a recognition    server.-   Clause 28: A monitoring data processing device as claimed in clause    27, wherein the first level of detail is metadata describing    properties of the event entity.-   Clause 29: A monitoring data processing device as claimed in clause    28, wherein the event entity is identified based upon the metadata.-   Clause 30: A monitoring data processing device as claimed in clause    27 to 29 including a comparator and a data store defining    notification events; and responsive to the identified event entity    matching a notification event, outputting a notification.-   Clause 31: A monitoring data processing device as claimed in clause    27, wherein the device receives a request from a remote server to    monitor an event entity at a second level of detail based on second    event data, wherein the second level of detail is more detailed than    the first level of detail, and outputting a notification comprising    the event data having the second level of detail.-   Clause 32: A recognition server comprising input circuitry for    receiving, at a first level of detail, first event data from a    plurality of data processing devices in a monitored region; a    processor for processing the first event data to identify the event    entity inferred by the event data; a comparator for comparing the    identified event entity with a data store defining notification    events; and responsive to the identified event entity matching a    notification event, an output for outputting a notification.-   Clause 33: A recognition server as claimed in clause 32, wherein the    first level of detail of first event data is metadata describing    properties of the event entity.-   Clause 34: A recognition server as claimed in clause 33, wherein the    event entity is identified based upon the metadata.-   Clause 35: A recognition server as claimed in clause 32, wherein in    response to the event entity matching a notification event,    requesting at a second level of detail second event data from the    data processing devices, wherein the second level of detail is more    detailed than the first level of detail, and outputting a    notification comprising the event data having the second level of    detail.-   Clause 36: A recognition server as claimed in clause 32, wherein    outputting a notification comprising the event data having the    second level of detail comprises a notification to a data processing    device having authorisation to access the event data having the    second level of detail.-   Clause 37: A recognition server as claimed in clause 32, wherein the    event data having the second level of detail identifies a movement    of the event entity.-   Clause 38: A recognition server as claimed in clause 32, wherein the    event data having the second level of detail identifies an image of    the event entity, the image being of a different resolution to the    event data having the first level of detail.-   Clause 39: A recognition server as claimed clause 32, wherein the    event data having the second level of detail identifies a sound of    the event entity.-   Clause 40: A network for monitoring an event entity comprising a    plurality of data processing devices comprising sensors for    monitoring the presence of event entities in a monitored region and    a data processor for generating first event data at a first level of    detail used to identify the event entity; and an output module for    communicating the first event data to a recognition server; the    recognition server comprising input circuitry for receiving, at a    first level of detail, first event data from the plurality of data    processing devices; a processor for processing the first event data;    a comparator for comparing the identified event entity with a data    store defining notification events; and responsive to the identified    event entity matching a notification event, an output for outputting    a notification.

As will be appreciated by one skilled in the art, the present techniquemay be embodied as a system, method or computer program product.Accordingly, the present technique may take the form of an entirelyhardware embodiment, an entirely software embodiment, or an embodimentcombining software and hardware. Where the word “component” is used, itwill be understood by one of ordinary skill in the art to refer to anyportion of any of the above embodiments.

Computer program code for carrying out operations of the presenttechniques may be written in any combination of one or more programminglanguages, including object oriented programming languages andconventional procedural programming languages.

For example, program code for carrying out operations of the presenttechniques may comprise source, object or executable code in aconventional programming language (interpreted or compiled) such as C,or assembly code, code for setting up or controlling an ASIC(Application Specific Integrated Circuit) or FPGA (Field ProgrammableGate Array), or code for a hardware description language such asVerilog™ or VHDL (Very high speed integrated circuit HardwareDescription Language).

The program code may execute entirely on the user's computer, partly onthe user's computer and partly on a remote computer or entirely on theremote computer or server. The program code may execute and run entirelyor partly on a sensor device, eg. a camera. The remote computer may beconnected to the user's computer through any type of network. Codecomponents may be embodied as procedures, methods or the like, and maycomprise sub-components which may take the form of instructions orsequences of instructions at any of the levels of abstraction, from thedirect machine instructions of a native instruction-set to high-levelcompiled or interpreted language constructs.

It will also be clear to one of skill in the art that all or part of alogical method according to embodiments of the present techniques maysuitably be embodied in a logic apparatus comprising logic elements toperform the steps of the method, and that such logic elements maycomprise components such as logic gates in, for example a programmablelogic array or application-specific integrated circuit. Such a logicarrangement may further be embodied in enabling elements for temporarilyor permanently establishing logic structures in such an array or circuitusing, for example, a virtual hardware descriptor language, which may bestored and transmitted using fixed or transmittable carrier media.

In one alternative, an embodiment of the present techniques may berealized in the form of a computer implemented method of deploying aservice comprising steps of deploying computer program code operable to,when deployed into a computer infrastructure or network and executedthereon, cause said computer system or network to perform all the stepsof the method.

In a further alternative, an embodiment of the present technique may berealized in the form of a data carrier having functional data thereon,said functional data comprising functional computer data structures to,when loaded into a computer system or network and operated upon thereby,enable said computer system to perform all the steps of the method.

It will be clear to one skilled in the art that many improvements andmodifications can be made to the foregoing exemplary embodiments withoutdeparting from the scope of the present technique.

1. A method of monitoring for the presence of an event entity in amonitored region comprising: receiving, at a first level of detail,first event data from at least one data processing device configured tomonitor at least a portion of a monitored region, the first event dataindicative of an event entity occurring in the monitored region andbeing an anonymized compression of raw information data collected bysaid at least one data processing device; processing the first eventdata to determine the presence of an event entity indicated by the eventdata; requesting, at a second level of detail, second event data,wherein the second level of detail is more detailed than the first levelof detail; and receiving a notification comprising the second event datahaving the second level of detail, wherein the second level of detail isrequested in response to the event entity matching a notification event,and wherein a recognition server has authorisation to access the secondevent data having the second level of detail; or wherein the secondlevel of detail is requested in response to the event entity matching anotification event, and wherein a recognition server has a policy toaccess the second event data having the second level of detail.
 2. Amethod as claimed in claim 1, wherein the first level of detail of firstevent data comprises metadata describing properties of the event entity.3. A method as claimed in claim 2, wherein the event entity isidentified based upon the metadata.
 4. A method as claimed in claim 1,including performing vector form extraction on the event data to providethe first level of detail.
 5. A method as claimed in claim 4, whereinthe first level of detail is a vector image representative of the eventdata.
 6. A method as claimed in claim 1, including communicating theevent data to a recognition server and wherein the recognition servercomprises a scenario identifier in communication with a decision modulefor determining a course of action associated with the notification. 7.A method as claimed in claim 6, wherein the recognition server comprisesa scenario identifier in communication with a vector event data store, avector scenario data store and a vector environment data store forcomparing the vector image with one or more of the data stores.
 8. Amethod as claimed in claim 6, including a predictive event generatormodule for analysing the scenario identifier and making a prediction ona likely outcome of a future event.
 9. A method as claimed in claim 1,including assessing event data with location data, historical dataand/or environmental conditions data.
 10. A method as claimed in claim1, including obtaining event data from a plurality of sensors, at leastone sensor being one of embedded IoT devices, image sensors, soundsensors, brightness sensors, odour sensors, temperature sensors,humidity sensors and proximity sensors, fitness trackers, PIR motiondetectors and mobile phones.
 11. A method as claimed in claim 1, whereinoutputting a notification includes implementing a predetermined courseof action.
 12. A method as claimed in claim 11, wherein thepredetermined course of action is an alert, alarm, security response ortracking the event entity.
 13. A method as claimed in claim 1, thepolicy including one or more of: recording images and/or sound,recording at different levels of detail or abstraction than the firstlevel of detail, outputting a notification.
 14. A method as claimed inclaim 1, including automatically implementing the policy based uponpredetermined criteria and, optionally, dynamically setting the policyto increase or decrease a level of abstraction.
 15. A monitoring dataprocessing device to monitor an event entity comprising: a dataprocessor for generating first event data at a first level of detailused to identify the event entity, the first event data being ananonymized compression of raw information data collected by a sensor;wherein the monitoring data processing device is operable to receive arequest to monitor an event entity at a second level of detail togenerate second event data, wherein the second level of detail is moredetailed than the first level of detail; and wherein the monitoring dataprocessing device is operable to output the second event data comprisinga notification to a data processing device being authorised to accessthe second event data having the second level of detail; or wherein themonitoring data processing device is operable to output the second eventdata comprising a notification to a data processing device having apolicy to access the second event data having the second level ofdetail.
 16. A monitoring data processing device as claimed in claim 15,wherein the first level of detail is metadata describing properties ofthe event entity.
 17. A monitoring data processing device as claimed inclaim 16, wherein the event entity is identified based upon themetadata.
 18. A recognition server to monitor an event entitycomprising: input circuitry for receiving, at a first level of detail,first event data from at least one processing device in a monitoredregion, the first event data being an anonymized compression of rawinformation data collected by the least one data processing device; aprocessor for processing the first event data to identify the evententity inferred by the event data; responsive to the event entitymatching a notification event, following a request, the input circuitryto receive, at a second level of detail, second event data from the atleast one data processing device, wherein the second level of detail ismore detailed than the first level of detail, and the recognition serverto output a notification comprising the second event data having thesecond level of detail; and wherein outputting a notification comprisingthe event data having the second level of detail comprises anotification to a data processing device having authorisation to accessthe event data having the second level of detail; or wherein outputtinga notification comprising the event data having the second level ofdetail comprises a notification to a data processing device having apolicy to access the event data having the second level of detail.
 19. Arecognition server as claimed in claim 18, wherein the first level ofdetail of first event data is metadata describing properties of theevent entity.